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Multiplicative noise removal is a challenging image processing problem. In this paper, we study a fractional-order nonlinear cross-diffusion system to denoise images corrupted by multiplicative Gamma noise. The system is coupled by two nonlinear, partial differential equations and gray level information, fractional derivative operator is considered in diffusion process to remove high level noise effectively while preserve structure details for texture images and SAR images. We prove the existence of weak solutions and other theoretical properties of the fractional-order cross-diffusion system under some hypotheses. In the numerical aspect, the model is implemented by virtue of the discrete fractional Fourier transform in the frequency domain. Experimental results show that the new system has a good compromise between multiplicative noise removal and structure preservation when compared to other well-known denoising methods. [ABSTRACT FROM AUTHOR]

• Phisically-based thermal infrared images rendering chain for artificial satellite targets. • Assessment and comparison of most relevant pixel-level VIS-TIR image fusion techniques. • Fused noisy image processing within the context of uncooperative space-craft relative navigation. State-of-the-art techniques for vision-based relative navigation rely on images acquired in the visible spectral band. Consequently, the accuracy and robustness of the navigation is strongly influenced by the illumination conditions. The exploitation of thermal-infrared images for navigation purposes is studied in the present paper, as a possible solution to improve navigation in close proximity with a target spacecraft. Thermal-infrared images depend on the thermal radiation emitted by the target, hence, they are independent from light conditions; however, they suffer from a poorer texture and a lower contrast with respect to visible ones. This paper proposes pixel-level image fusion to overcome the limitations of the two types of images. The two source images are merged into a more informative one, retaining the strengths of the distinguished sensing modalities. The contribution of this work is twofold: firstly, a realistic thermal infrared images rendering tool for artificial targets is implemented; secondly, different pixel-level visible-thermal infrared images fusion techniques are assessed through qualitative and quantitative performance metrics to ease and improve the subsequent image processing step. The work presents a comprehensive evaluation of the best fusion techniques for on-board implementation, paving the way to the development of a multispectral end-to-end navigation chain. [ABSTRACT FROM AUTHOR]

The extraction of cellular structure feature on the spherical premixed flame surface faces accuracy challenges. The Schlieren technique was employed to obtain the hydrogen-air premixed spherical flames images in a constant volume vessel at room temperature and atmospheric pressure under an equivalent ratio of 0.8 in this work. A bio-inspired Cellpose 2.0, driven by deep learning, is innovatively introduced to train the cell segmentation model in the combustion field. After labeling and training cells of different shapes and sizes, an efficient and accurate model suitable for cell feature extraction was finally obtained to identify and quantify various cells characteristics, such as number, size, and distribution. Results show that the average precision (AP) during the model online pre-training process reaches 0.625. Meanwhile, the critical flame radius of transition acceleration obtained is 36 mm and the crack length tends to grow linearly after the flame radius exceeds this critical point. Additionally, the average cell area gradually converges to a stable value after the flame radius exceeds the uniform cellularity critical radius. The cell segmentation model obtained in this work can be further used to train different spherical flames under various conditions, helping to develop hydrogen combustion and explosion modelling. [Display omitted] • First application of Cellpose 2.0 in combustion image process yields AP of 0.625 • Quantitative cell information on H 2 -Air flame surfaces achieved with deep learning. • Cells number rapid increase after the flame exceeds critical radius of transition acceleration. • Average cell area gradually converges to a stable value after uniform cellularity critical radius. • Flame propagation exhibits a self-accelerating trend after transition acceleration critical radius. [ABSTRACT FROM AUTHOR]

Over the past two decades, vibrational resonance has garnered significant interest and evolved into a prominent research field. Classical vibrational resonance examines the response of a nonlinear system excited by two signals: a weak, slowly varying characteristic signal, and a fast-varying auxiliary signal. The characteristic signal operates on a much longer time scale than the auxiliary signal. Through the cooperation of the nonlinear system and these two excitations, the faint input can be substantially amplified, showcasing the constructive role of the fast-varying signal. Since its inception, vibrational resonance has been extensively studied across various disciplines, including physics, mathematics, biology, neuroscience, laser science, chemistry, and engineering. Here, we delve into a detailed discussion of vibrational resonance and the most recent advances, beginning with an introduction to characteristic signals commonly used in its study. Furthermore, we compile numerous nonlinear models where vibrational resonance has been observed to enhance readers' understanding and provide a basis for comparison. Subsequently, we present the metrics used to quantify vibrational resonance, as well as offer a theoretical formulation. This encompasses the method of direct separation of motions, linear and nonlinear vibrational resonance, re-scaled vibrational resonance, ultrasensitive vibrational resonance, and the role of noise in vibrational resonance. Later, we showcase two practical applications of vibrational resonance: one in image processing and the other in fault diagnosis. This presentation offers a comprehensive and versatile overview of vibrational resonance, exploring various facets and highlighting promising avenues for future research in both theory and engineering applications. [ABSTRACT FROM AUTHOR]

• Multispectral imaging for study of the Sarajevo Haggadah. • Non-invasive scientific analysis of medieval illuminated Hebrew manuscripts. • Application of image processing techniques to recover erased text. Over the last two decades, multispectral imaging (MSI) has established itself as the most important tool in recovering illegible text in erased or damaged manuscripts, and to a lesser extent as an aid to the codicological study of manuscript supports and media. Rarely, however, have spectral imaging initiatives involved Hebrew illuminated manuscripts. In 2022, an international team of scholars, imaging scientists, and conservators from the National Museum of Bosnia and Herzegovina and the Lazarus Project of the University of Rochester imaged the Sarajevo Haggadah multispectrally. Produced for a Jewish commissioner and housed at the National Museum of Bosnia and Herzegovina, this 14th-century illuminated manuscript contains, inter alia , a partially erased text of a sale contract on folio 106v* that is now newly legible. Codicological details of the manuscript – ink, stains, type of animal used for parchment – have also benefitted from material analysis via MSI. Herein, we describe the MSI process of the Sarajevo Haggadah and the application of image processing algorithms to recover the erased text on folio 106v*. A complete text of a sale contract is revealed for the first time, providing significant information about the ownership and provenance of the Sarajevo Haggadah at the beginning of the 16th century. [ABSTRACT FROM AUTHOR]

Diffusion MRI of the spinal cord (SC) is susceptible to geometric distortion caused by field inhomogeneities, and prone to misalignment across time series and signal dropout caused by biological motion. Several modifications of image acquisition and image processing techniques have been introduced to overcome these artifacts, but their specific benefits are largely unproven and warrant further investigations. We aim to evaluate two specific aspects of image acquisition and processing that address image quality in diffusion studies of the spinal cord: susceptibility corrections to reduce geometric distortions, and cardiac triggering to minimize motion artifacts. First, we evaluate 4 distortion preprocessing strategies on 7 datasets of the cervical and lumbar SC and find that while distortion correction techniques increase geometric similarity to structural images, they are largely driven by the high-contrast cerebrospinal fluid, and do not consistently improve the geometry within the cord nor improve white-to-gray matter contrast. We recommend at a minimum to perform bulk-motion correction in preprocessing and posit that improvements/adaptations are needed for spinal cord distortion preprocessing algorithms, which are currently optimized and designed for brain imaging. Second, we design experiments to evaluate the impact of removing cardiac triggering. We show that when triggering is foregone, images are qualitatively similar to triggered sequences, do not have increased prevalence of artifacts, and result in similar diffusion tensor indices with similar reproducibility to triggered acquisitions. When triggering is removed, much shorter acquisitions are possible, which are also qualitatively and quantitatively similar to triggered sequences. We suggest that removing cardiac triggering for cervical SC diffusion can be a reasonable option to save time with minimal sacrifice to image quality. [ABSTRACT FROM AUTHOR]

• The presence of frost on the evaporator coil degrades the cooling performance. • K-means image segmentation-based frost detection and thickness estimation is proposed. • Frost thickness was estimated using image analysis with an error of 13.69%. • Results were compared with conventional sensors and existing image processing methods. • The method can serve as a reliable feedback source for efficient defrosting. Frost accumulation is a common problem when moisture in the air condenses and freezes on surfaces like heat exchange tubes of refrigeration units. Frost accumulation negatively impacts heat exchange by disrupting the process, reducing system efficiency, and causing operational issues. Therefore, defrosting is mandatory to maintain the rated performance; however, modern automatic defrosting systems rely on sophisticated sensors for frost quantification. These sensors are susceptible to degraded performance with the passage of time under varying environmental conditions. To this end, we introduce a robust and generic image processing-based solution that relies on building a data-driven regression-based model for frost detection and thickness estimation. We evaluated the effectiveness of the proposed method on a newly collected dataset with encouraging performance in terms of a low error margin of 13.69% when compared to conventional capacitive and photoelectric sensors-based frost thickness estimation with error margins of 15.17% and 17.5%, respectively. Similarly, other image processing-based methods, such as Global thresholding, Adaptive mean, and Adaptive gaussian thresholding for segmentation, were compared with the proposed method. Deviations in the error margins were found to be 19.94%, 28.96%, and 27.85%, respectively. These findings highlight the appropriateness of employing K-means for estimating frost thickness. [ABSTRACT FROM AUTHOR]

[Display omitted] Field path planning provides the basis for the autonomous navigation of agricultural vehicles. Existing path planning approaches are constrained to a 2D plane, disregarding the impact of terrain factors on navigation tasks. Furthermore, these methods generate non-global paths, as evidenced by their inability to accommodate the headland turning area while performing path planning based on the actual crop growth in the farmland. Low-altitude remote sensing technology, characterized by its high spatial resolution, timely data acquisition, and strong terrain perception, holds great potential for application in autonomous navigation. This study aims to integrate low-altitude remote sensing technology with path-planning tasks for agricultural vehicles by constructing oblique photography models of fields. The objective is to achieve global 3D path planning leveraging advanced methodologies rooted in deep learning and image processing. Four main steps were included in the proposed method. Low-altitude remote sensing models of field blocks were constructed first. Secondly, the models were converted to image patches for dataset establishment. Thirdly, primary crop row identification was conducted by semantic segmentation. Finally, global 3D paths covering the farmland and headland areas were generated. Tea fields in hilly areas were used to test the algorithm. Experiments revealed that the proposed method could be adapted to different field shapes, row numbers, row spacing, and vehicle turning radii. The proposed method uses deep learning and image processing as the primary technical tools but goes beyond traditional crop row detection to form a global path-planning strategy. In addition, the elevation information enabled by the 3D detection manner allows agricultural vehicles to gain a comprehensive understanding of both their own position and that of their destination. The dataset and the code are available at: https://github.com/ZeroHeading/Global-3D-path-generation.git. [ABSTRACT FROM AUTHOR]

Modic changes are pathologies that are common in the population and cause low back pain. The aim of the study is to analyze the modic changes detected in magnetic resonance imaging (MRI) using deep learning modalities. The sagittal T1, sagittal and axial T2-weighted lumbar MRI images of 307 patients, of which 125 were female and 182 were male, aged 19–86 years, who underwent MRI examination between 2016–2021 were analyzed. Modic changes (MC) were categorized and marked according to signal changes. Our study consists of 2 independent stages: classification and segmentation. The categorized data were first classified using convolutional neural network (CNN) architectures such as DenseNet-121, DenseNet-169, and VGG-19. In the next stage, masks were removed by segmentation using U-Net, which is the CNN architecture, with image processing programs on the marked images. During the classification stage, the success rates for modic type 1, type 2, and type 3 changes were 98%, 96%, 100% in DenseNet-121, 100%, 94%, 100% in DenseNet-169, and 98%, 92%, 97% in VGG-19, respectively. At the segmentation phase, the success rate was 71% with the U-Net architecture. Evaluation of MRI findings of MC in the etiology of lower back pain with deep learning architectures can significantly reduce the workload of the radiologist by providing ease of diagnosis. [ABSTRACT FROM AUTHOR]

Retinal bipolar and amacrine cells receive visual information from photoreceptors and participate in the first steps of image processing in the retina. Several studies have suggested the operation of aerobic glycolysis and a lactate shuttle system in the retina due to the high production of this metabolite under aerobic conditions. However, whether bipolar cells form part of this metabolic circuit remains unclear. Here, we show that the monocarboxylate transporter 2 is expressed and functional in inner retinal neurons. Additionally, we used genetically encoded FRET nanosensors to demonstrate the ability of inner retinal neurons to consume extracellular lactate as an alternative to glucose. In rod bipolar cells, lactate consumption allowed cells to maintain the homeostasis of ions and electrical responses. We also found that lactate synthesis and transporter inhibition caused functional alterations and an increased rate of cell death. Overall, our data shed light on a notable but still poorly understood aspect of retinal metabolism. [ABSTRACT FROM AUTHOR]

Generation of hidden attractor with complicated phase portrait in chaotic system with no equilibrium has presented a new research focus in the past decade. However, the existing approaches usually follow the rule that you reap what you sow, i.e., taking an no-equilibrium chaotic system as the seed. In this paper, a novel approach to the generation of no-equilibrium multi-fold hidden attractors is presented. The offset boosting and the operation of complex number are applied to several cases, including the seed chaotic systems with no equilibrium, with single unstable node, or with single stable node-focus. In specific, the offset boosting shifts the single non-zero equilibrium to the origin, then the operation of complex number rules out the equilibrium in the folded system and generates multi-fold trajectories. The highlight is that the proposed approach is not limited to the no-equilibrium seed system, but also applicable for different types of single-equilibrium seed systems, even the seed system with a single line of equilibrium. The detailed numerical simulations and experiments confirmed the feasibility of the proposed approach. Finally, an image encryption algorithm and its practical microcontroller-based implementation are presented to support the potential application. • Systematic approach for folding trajectory of chaotic system. • New multi-fold chaotic hidden attractors with no equilibrium. • New chaos-based encryption algorithm for image security. • Dynamic memory scheme for microcontroller implementation. [ABSTRACT FROM AUTHOR]

Poisson image deblurring, which aims to restore the latent image from its blurred and noisy observation, has drawn significant attention in image processing. Due to its ill-posed nature, enhancing image quality often involves incorporating a well-defined prior to effectively regularize the ill-posed inverse problem. Building upon the framelet system, we propose a frame-based nonconvex regularization method for Poisson image deblurring. The method is formulated by combining a data-fitting term with the difference of two norms, namely ℓ 1 and ℓ 2 , on the latent image. We solve the optimization problem by combining the difference of convex functions algorithm (DCA) with the alternating direction method of multipliers (ADMM), establishing its convergence. We further employ a simulated annealing procedure and show that proposed algorithm almost certainly converges to a global minimum. Two different approaches are employed to handle the frame-based ℓ 1 norm within the ADMM framework. In particular, the frame-based nonconvex regularization method is also considered for the blind Poisson problem. An effective recovery model and its algorithm are presented. Experimental results demonstrate the effectiveness of our proposed models compared with other models in terms of quantitative metrics and visual quality. • Frame-based nonconvex regularization model is proposed to eliminate Poisson noise and blur from degraded image. • An efficient algorithm with convergence guarantees is proposed. • We study an accelerated algorithm and explore the blind Poisson problem. • Experimental results demonstrate the effectiveness of our proposed models compared with other models. [ABSTRACT FROM AUTHOR]

Solving tensor systems is a common task in scientific computing and artificial intelligence. In this paper, we propose a tensor randomized average Kaczmarz method with adaptive parameters that exponentially converges to the unique least Frobenius norm solution of a given consistent tensor system under the t-product structure. In order to accelerate convergence, a tensor average Kaczmarz method based on stochastic heavy ball momentum technique (tAKSHBM) is proposed. The tAKSHBM method utilizes iterative information to update parameters instead of relying on prior information, addressing the problem in the adaptive learning of parameters. Additionally, the tAKSHBM method based on Fourier transform is proposed, which can be effectively implemented in a distributed environment. It is proven that the iteration sequences generated by all the proposed methods are convergent for given consistent tensor systems. Finally, we conduct experiments on both synthetic data and practical applications to support our theoretical results and demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The enhancement of low-quality images is both a challenging task and an essential endeavor in many fields including computer vision, computational photography, and image processing. In this paper, we propose a novel and fully explainable method for image enhancement that combines spatial selection and histogram equalization. Our approach leverages tree-search theory and deep reinforcement learning to iteratively select areas to be processed. Extensive experimentation on two datasets demonstrates the quality of our method compared to other state-of-the-art models. We also conducted a multi-user experiment which shows that our method can emulate a variety of enhancement styles. These results highlight the effectiveness and versatility of the proposed method in producing high-quality images through an explainable enhancement process. • A fully explainable image enhancement method based on reinforcement learning. • The method alternates spatial selection and histogram equalization through deep RL. • An extensive experimentation shows that our method is competitive with SOTA methods. [ABSTRACT FROM AUTHOR]

Higher magnetic field strength introduces stronger magnetic field inhomogeneities in the brain, especially within temporal lobes, leading to image artifacts. Particularly, T2-weighted fluid-attenuated inversion recovery (FLAIR) images can be affected by these artifacts. Here, we aimed to improve the FLAIR image quality in temporal lobe regions through image processing of multiple contrast images via machine learning using a neural network. Thirteen drug-resistant MR-negative epilepsy patients (age 29.2 ± 9.4y, 5 females) were scanned on a 7 T MRI scanner. Magnetization-prepared (MP2RAGE) and saturation-prepared with 2 rapid gradient echoes, multi-echo gradient echo with four echo times, and the FLAIR sequence were acquired. A voxel-wise neural network was trained on extratemporal-lobe voxels from the acquired structural scans to generate a new FLAIR-like image (i.e., deepFLAIR) with reduced temporal lobe inhomogeneities. The deepFLAIR was evaluated in temporal lobes through signal-to-noise (SNR), contrast-to-noise (CNR) ratio, the sharpness of the gray-white matter boundary and joint-histogram analysis. Saliency mapping demonstrated the importance of each input image per voxel. SNR and CNR in both gray and white matter were significantly increased (p < 0.05) in the deepFLAIR's temporal ROIs, compared to the FLAIR. The gray-white matter boundary sharpness was either preserved or improved in 10/13 right-sided temporal regions and was found significantly increased in the ROIs. Multiple image contrasts were influential for the deepFLAIR reconstruction with the MP2RAGE second inversion image being the most important. The deepFLAIR network showed promise to restore the FLAIR signal and reduce contrast attenuation in temporal lobe areas. This may yield a valuable tool, especially when artifact-free FLAIR images are not available. • 7 T FLAIR often suffers from signal dropout in temporal lobes. • A subject-specific neural network to reduce the artifacts in post-processing. • The network reconstructs per voxel the FLAIR signal from other structural scans. • Improved SNR and CNR were observed in the attenuated regions. • Further developments may yield deepFLAIR a helpful tool to combat signal dropout. [ABSTRACT FROM AUTHOR]

ADITYA-L1 is India's first dedicated mission to observe the Sun and its atmosphere from a halo orbit around L1 point. Visible emission line coronagraph (VELC) is the primary payload on board Aditya-L1 to observe the Sun's corona. VELC is designed as an internally occulted reflective coronagraph to meet the observational requirements of wide wavelength band and the field of view close to the solar limb (1.05 R Sun ). Images of the solar corona in continuum and spectra in three emission lines 5303 Å [Fe xiv], 7892 Å [Fe xi] and 10747 Å [Fe xiii] obtained with high cadence are to be analyzed using software algorithms automatically. A reasonable part of these observations will be made in a synoptic mode in which the data are processed through a standard pipeline and the resulting products will be made available for public use. The procedure involves the calibration of instrument and detectors, converting the images into the FITS format, correcting the images and spectra for the instrumental effects, and aligning the images (in terms of position angles and image scales) etc. Then, develop image processing algorithms to detect the occurrence of energetic events using continuum images. Also derive physical parameters, such as temperature and velocity structure of solar corona using emission line observations. Here, we describe the calibration of detectors and the development of software algorithms to detect the occurrence of CMEs and analyze the spectroscopic data. [ABSTRACT FROM AUTHOR]

The melting of Antarctic glaciers has become a significant issue as a result of global climate change. Algae on the Antarctic ice/snow is an important part of terrestrial photosynthetic organisms. Monitoring and tracking these algal blooms is crucial for understanding the melting of glaciers in the region. Due to the climatic and natural conditions of the region, traveling to and arranging logistics for monitoring and observing snow algae in the Antarctic continent becomes extremely challenging. To overcome these challenges, a novel algorithm has been developed and designed to automatically detect and analyze green algae (Chlorella sp.) from satellite images. Leveraging the vast and free available data from the Sentinel-2 satellite, the algorithm utilizes its high spectral resolution capabilities, capturing invaluable information from various spectral bands. The algorithm was formulated based on the image obtained on February 28, 2017, where green algae formations were intensively seen in the Ryder Bay. The algorithm was developed based on rule-based detection of algae, with the usage of reflection values from the areas where ground truth was established on this date. The developed algorithm was coded and tested using Python version 3.9. The accuracy analysis of the algorithm was conducted using overall accuracy (OA), F1 score, and Kappa statistical test. As a result of the analysis, the OA, F1 score, and Kappa statistic values were calculated as %91, %88.82-%95.27, and 0.901, respectively. [ABSTRACT FROM AUTHOR]

Therapeutic proteins such as monoclonal antibodies (mAb) are known to form aggregates due to various factors. Phosphate buffered saline (PBS), human serum, and human serum filtrate (HSF) are some of the models used to analyze mAb stability in physiologically relevant in-vitro conditions. In this study, aggregation of mAb in PBS and models derived from body fluids seeded with mAb samples subjected to various stresses were compared. Samples containing mAb subjected to pH, temperature, UV light, stirring, and interfacial agitation stress were seeded into different models for 2 case studies. In the first case study, %HMW (high molecular weight species) of mAb in PBS and HSF were compared using size exclusion chromatography. It was found that change in %HMW was higher in PBS compared to HSF. For example, PBS containing mAb that was subjected to UV light stress showed change in HMW by >10 % over 72 h, but the change was <5 % in HSF. In second case study, aggregates particles of FITC tagged mAb were monitored in PBS and serum using fluorescence microscope image processing. It was found that PBS and serum containing mAb subjected to stirring and interfacial agitation resulted in aggregates of >2 µm size, and average size and percentage number of particles having >10 µm size was higher in serum compared to PBS at all analysis time point. Overall, it was found that aggregation of mAb in PBS was different from that in human body fluids. Second case study also showed the importance of advanced strategies for further characterization of mAb in serum. [Display omitted] • Models such as PBS, serum, and HSF have been used to mimic in-vitro physiological conditions for stability assessment of mAb. • PBS and HSF seeded with mAb samples subjected to different stress showed that HMW change in PBS was higher compared to HSF. • Sub-visible aggregates of mAb having >2 µm size was found at different time points in PBS and serum seeded with mAb samples subjected to stirring and interfacial agitation stress. • In case of serum and PBS seeded with mAb samples subjected to stirring and interfacial agitation, % number of aggregates having >10 µm size was higher in serum compared to PBS. • Overall, aggregation of mAb was different in PBS compared to models derived from human body. [ABSTRACT FROM AUTHOR]

• The inner information of historically significant textiles is essential for the preservation and restoration processes. • Radiography testing (RT) reveals hidden damage to under-sewing layers, clothing design and connections. • X-ray radiographs of the textile suffer from low contrast due to low density of the objects. • An image processing technique, e.g., Automatic Color Enhancement (ACE) methods has been implemented to improve the contrast of radiographs and reveal more information. • The conservators and radiographers can utilize this information for the restoration process. The availability of information as to the inner structures and condition of historically significant textiles and clothing items is essential for monitoring, preservation and restoration processes. Visual inspection is usually the initial step in identifying any damage, such as tearing or rotting, but it is not sufficient for examining of detail below the surface of the objects. Radiography Testing (RT) is a useful non-destructive approach, capable of revealing hidden damage to under-sewing layers, clothing design, and connections, providing crucial information for developing a conservation plan. However, due to the mainly low atomic number of the constituting elements of textile material and also the low thickness of such items, X-ray radiographs suffer from low imaging contrast, which is further compromised by noise due to the unavoidable detection of scattered X-rays, which introduces fogginess in the acquired radiographs. Using digital RT images as a starting point, two image processing approaches using histogram equalization and the more complex Automatic Color Enhancement (ACE) methods were applied to enhance the imaging contrast of the original radiographs. The results demonstrated the effectiveness of these methods in better identifying defects, hidden designs, motifs, and destructive regions within the inner layers of textiles. It was found that the ACE method produced better outcomes than the simpler histogram equalization approach. Results indicated that the ACE processed image could be favored over the simpler histogram equalization approach by its enhanced capability of targeted expansion of the scale for evaluation of deterioration. The conservators and radiographers were able to utilize the further and complementary capability towards improved object assessment, monitoring, and restoration procedures. [ABSTRACT FROM AUTHOR]

In this paper, image processing techniques are employed to investigate the growth and detachment characteristics of initial bubbles in vertical glass tubes. The study explores the effects of different continuous phases (water and Therminol®66), nozzle diameters, air flow rates, and continuous phase liquid levels on the detachment diameter and shape of bubbles. Furthermore, based on the detachment characteristics obtained under various experimental conditions, a novel predictive model for bubble detachment diameter is established in both water and Therminol®66. The results show that the bubble detachment diameter in the continuous phase therminol® 66 is larger than the bubble detachment diameter in water, and that the shape of the bubbles in therminol® 66 is close to spherical, with an ellipsoidal shape in water. With the increase in nozzle diameter and airflow, the bubble detachment diameter increases, and the shape is stretched in the vertical direction. With the increase in the continuous phase liquid level, the bubble detachment diameter decreases, and the shape change is not significant. In addition, the accuracy of the bubble detachment diameter prediction model was validated through experimental data. Compared with the experimental values, the average absolute errors were 5.04% and 5.11% in water and Therminol®66, respectively. This study aims to investigate the characteristics of initial bubble growth and detachment, providing valuable references for nozzle design. [Display omitted] • The growth characteristics of initial bubbles under different working fluids are studied • The shape and diameter of the bubble detachment process are studied under different operating conditions. • A new bubble detachment diameter prediction model is established [ABSTRACT FROM AUTHOR]

L'impression 3D a investi le champ médical dès le début du 21e siècle. Elle s'est démocratisée au fil des années pour devenir aujourd'hui un outil accessible à coût quasiment nul à condition d'avoir une imprimante 3D à disposition. Le chirurgien peut ainsi selon ses besoins l'intégrer facilement à sa pratique et ses techniques au bloc opératoire, sous réserve d'un apprentissage des logiciels de traitement de l'image tridimensionnelle. Afin d'illustrer l'ensemble du processus, de la genèse et traitement de l'image tridimensionnelle à son application au bloc opératoire, nous décrivons le cas d'un patient amputé du pavillon de l'oreille gauche, dont la reconstruction a été guidée par un modèle 3D imprimé à partir de son oreille droite. 3D printing has been used in the medical field since the beginning of the 21st century. Over the years, it has been democratized and has become an accessible tool at almost no cost, provided that a 3D printer is available. The surgeon can thus easily integrate it into his practice and techniques in the operating room, provided that he learns to use 3D image processing software. In order to illustrate the whole process, from the genesis and processing of the 3D image to its application in the operating room, we describe the case of a patient with a left auricle amputation, whose reconstruction was guided by a 3D model printed from his right ear. [ABSTRACT FROM AUTHOR]

The interest of science community towards planet Mars has progressively increased in past decades. Rovers are already doing an excellent job in exploring the Martian surface environment and showed that the Red Planet is not suited for life. However, there is a possibility of enabling human exploration by having astronauts take shelter in habitats located within lava tubes, which can provide protection against radiation and dust storms. These tubes are extremely hard to explore with rovers, thus a flying vehicle will be more suitable for such tasks. INGENUITY (Martian helicopter) had already demonstrated that it is possible to fly on Mars by doing small and simple manoeuvres. Based on NASA's design, this article will introduce the development of guidance, navigation, and control operations of a Martian Inspection Drone (MID) that will be capable of finding the lava tube and scan it in real time using a RealSense camera. Linux based system was chosen in the development process, which allow ROS catkin workspace to connect ArduPilot and Gazebo. The navigation is accomplished using a combination of an inertial measurement unit, a camera, and a laser rangefinder. The flight computer runs an optical flow algorithm for control and navigation purpose. Also, a convolutional neural network is used to perform lava tube entrance detection and inspection. • Martian flying drone simulated with ArduPilot, Gazebo and ROS. • Martian environment simulation. • Martian lava tube detection using convolutional neural networks. • Martian lava tube inspection using a realsense camera. [ABSTRACT FROM AUTHOR]

• Photometric correction of IIRS images obtained from the Chandrayaan-2 mission. • IIRS data were found to be comparatively darker than Chandrayaan-1 M3. • Derived IIRS reflectances show minimal variation compared to M3 values. • Derived phase function fits for 950, 1500, and 1700 nm channels have high R2. • Absence of major artifacts (including cross-tracks) on IIRS data. India's second mission to the Moon, Chandrayaan-2, carried one of the most advanced imaging spectrometers ever sent to image a planetary surface – the IIRS (Imaging Infra-Red Spectrometer) – a 250 band spectrometer developed at the Space Applications Centre (SAC), ISRO, India. In this study, IIRS data between 800 and 2000 nm have been used to derive photometrically corrected reflectance from calibrated radiance data supplied by ISRO's online archive (Pradan) of Chandrayaan-2 data. Additionally, the photometric properties of three IIRS bands (950, 1500, and 1700 nm) have been studied in detail. Since the images obtained by IIRS are at different viewing geometry orientations, the purpose of performing a photometric correction on IIRS images is to re-project each pixel to a standard viewing geometry of incidence angle, i = 30°, emission angle, e = 0° and, phase angle, α = 30°. In order to correct the lunar limb darkening effect, a combination of the Lommel-Seeliger function and McEwen's model was used. In addition, topographic data from the merged Digital Elevation Model of Lunar Reconnaissance Orbiter – Lunar Orbiter Laser Altimeter (LRO-LOLA) and SELENE Terrain Camera (TC) were utilized to remove effects from local topography. A photometric function was then applied on four images captured by IIRS which were included in the first public release of data in August 2021. Radiance and reflectance (obtained after implementing photometric correction on IIRS images) values were compared with L1 and L2 M3 (Moon Mineralogy Mapper) data from Chandrayaan-1. It was found that although IIRS images are much darker than M3, corrected IIRS reflectance images had a much smaller variation in terms of reflectance values with M3. Moreover, the associated spectral reddening and reflectance values are within range when compared with data from other lunar missions. In addition, the reflectance spectra derived from the corrected IIRS images have been plotted for each image. However, the spectra exhibit significantly deeper absorptions than their corresponding M3 data for the same pixel. Phase functions derived from three bands have a good correlation with the reflectance values and phase angles. Consequently, this function can be applied to all IIRS images as and when more data are released to the public. [ABSTRACT FROM AUTHOR]

To investigate the utility of whole-tumor histogram analysis based on multiparametric MRI in distinguishing pure mucinous breast carcinomas (PMBCs) from fibroadenomas (FAs) with strong high-signal intensity on T2-weighted imaging (T2-SHi). The study included 20 patients (mean age, 55.80 ± 15.54 years) with single PBMCs and 29 patients (mean age, 42.31 ± 13.91 years) with single FAs exhibiting T2-SHi. A radiologist performed whole-tumor histogram analysis between PBMC and FA groups with T2-SHi using multiparametric MRI, including T2-weighted imaging (T2WI), diffusion weighted imaging (DWI) with apparent diffusion coefficient (ADC) maps, and the first (DCE_T1) and last (DCE_T4) phases of T1-weighted dynamic contrast-enhanced imaging (DCE) images, to extract 11 whole-tumor histogram parameters. Histogram parameters were compared between the two groups to identify significant variables using univariate analyses, and their diagnostic performance was assessed by receiver operating characteristic (ROC) curve analysis and logistic regression analyses. In addition, 15 breast lesions were randomly selected and histogram analysis was repeated by another radiologist to assess the intraclass correlation coefficient for each histogram feature. Pearson's correlation coefficients were used to analyze the correlations between histogram parameters and Ki-67 expression of PMBCs. For T2WI images, mean, median, maximum, 90th percentile, variance, uniformity, and entropy significantly differed in PBMCs and FAs with T2-SHi (all P < 0.05), yielding a combined area under the curve (AUC) of 0.927. For ADC maps, entropy was significantly lower in FAs with T2-SHi than in PMBCs (P = 0.03). In both DCE_T1 and DCE_T4 sequences, FAs with T2-SHi showed significantly higher minimum values than PBMCs (P = 0.007 and 0.02, respectively). The highest AUC value of 0.956 (sensitivity, 0.862; specificity, 0.944; positive predictive value, 0.962; negative predictive value, 0.810) was obtained when all significant histogram parameters were combined. Whole-tumor histogram analysis using multiparametric MRI is valuable for differentiating PBMCs from FAs with T2-SHi. • In clinical practice, fibroadenomas (FAs) with high-signal intensity on T2WI are easily confused with pure mucinous breast carcinomas (PMBCs). • Whole-tumor histogram parameters derived from T2WI, ADC, and DCE could distinguish PMBCs from FAs with T2-SHi. • Satisfactory diagnostic performance was obtained when all significant histogram parameters derived from multiparametric MRI were combined. [ABSTRACT FROM AUTHOR]

Operating processes involving liquid–liquid systems requires a sound understanding of fluid dynamics, mass transfer, and reaction kinetics, which strongly depend on droplet size distribution (DSD). Optical measurement is the most commonly used technique for DSD determination, followed by an appropriate image-processing method. However, the image-processing method is parameterized for a specific image quality, which varies depending on operating conditions and the setup of optical hardware. Generalizing the image-processing method Mask Region-Based Convolutional Neural Network (MRCNN) has the potential to robustly determine DSDs independently from varying image qualities. This work applies three generalization methods, namely augmentation, pre-processing, and training domain diversification, to generalize MRCNN. The droplet detection performance of the resulting generalized MRCNN model is compared with the manual processing for eight image qualities. The generalization of MRCNN by training domain diversification results in a robust model with high DSD detection accuracy. Averaged over the eight image qualities, the relative error in characteristic DSD properties is: Sauter mean diameter – 3 %, minimum diameter – 4 %, and maximum diameter – 10 %. Since MRCNN shows an accurate and robust processing performance independent of the photo-optical measurement equipment and operating conditions, it can be implemented in continuous online monitoring of the DSD without training on specific image quality. • Training data diversification improves the generalization ability of MRCNN. • MRCNN models trained on single image qualities show poor extrapolation performance. • Similar robust detection performance of generalized MRCNN model in interpolation and extrapolation. [ABSTRACT FROM AUTHOR]

The problem of monitoring space debris (SD) of anthropogenic origin is discussed to ensure the safety of near-Earth space environment exploration. For dynamic low-orbit SD fragments, acquisition and processing technologies of short-exposure image series, obtained in a non-redundant aperture synthesis array, are proposed and investigated. Specific features of the optical transfer function (OTF) of the non-redundant array, namely, its "insular" character, lead to difficulties in restoring the spatial spectrum of the control object, undistorted by the atmosphere, in the whole spatial-frequency domain of the array. It is shown that to restore the object's spatial spectrum module, undistorted by the atmosphere, in the whole spatial frequency domain of the array, it is necessary to use algorithm of the Labeyrie method, modified by the authors of the present work. To restore the phase of the spatial spectrum, it is necessary to use the algorithms of unfolding the closed phase equations of the triple correlations method. By physical modeling of the formation and registration processes of analog images in the atmosphere-array system and subsequent statistical processing of digitized images, experimental evidence is given of the effectiveness of the proposed restoring technologies to achieve high resolution of SD fragments in the array, aimed at their subsequent identification and removal. Recommendations are given for practical implementation of the considered promising construction and application technologies of optical systems to control SD fragments. • The problem of space debris (SD) control is investigated to ensure the safety of space flights. • Technologies for short-exposure images of control objects in a non-redundant aperture synthesis array for SD were proposed. • Algorithms of digital processing and restoration of the diffraction images of control objects in the array were developed. • Restoring technologies of SD image processing in non-redundant aperture synthesis array were studied by PC simulation methods. • High resolution of the observed SD fragments for their subsequent identification and removal were experimentally shown. [ABSTRACT FROM AUTHOR]

The complex flow of a supersonic jet (Mach number 2.4–2.5) around the blunt aerodynamic body (consisting of a cylinder with a diameter of 16 mm and a truncated cone) was studied using the high-speed shadowgraph technique and computer vision-based visual data processing. The problem considered is directly related to the rocket and space systems stages safe separation problem in a sufficiently dense atmosphere. For systems with a sequential arrangement of stages, their separation often occurs under the rocket engine jet action. The occurrence of lateral forces in violation of axial symmetry can lead to overturning of the discarded stage and undesirable interaction with the outgoing stage. The goals of the paper were to study the pulsation characteristics of the incoming supersonic jet streamlining the body, as well as the pulsations of the bow shock wave in both axisymmetric and asymmetric flows. The recording frame rate in the experiments was up to 775 000 fps. Computer vision and various machine learning approaches were used to process a large number of shadowgraph images. Canny edge detection and Hough transform, as well as a convolutional neural network (CNN) based on YOLOv2 architecture, were used to automatically track the position of the bow shock relative to the body. Jet sound generation was studied - the amplitude dependence on the distance from the nozzle and the evolution in time. The position of the bow shock relative to the streamlined body and flow pulsations over time were analyzed. Bow shock pulsation spectra at zero angle of attack were compared to those at a small angle of attack (α = 2.5°–3°). The power spectral density was also estimated. The slope of the spectra for the zero angle of attack case was found to be horizontal and the spectra for the small angle of attack case had a slope close to −5/3. The strongest of the measured bow shock frequencies were found to be close to those of the jet mixing layer pulsations. The hardware signal spectrum was also measured and this signal can be considered as noise. The described approach makes it possible to obtain the physical characteristics of the flow at any point of interest. It was also shown that the automation of flow visualization experiments using computer vision techniques allows to increase the speed of data processing and obtaining new physical information, which may be important for engineers developing aircraft and spacecraft technologies. • Supersonic jet flow around a blunt body was studied experimentally and also numerically. • Computer vision tools were applied to supersonic flow investigation. • Spectra of jet generated acoustic waves were determined. • Time dependent bow shock positions were automatically measured by the neural network. • Amplitudes of bow shock pulsations and flow fluctuations depend on the angle of the body relative to the jet direction. [ABSTRACT FROM AUTHOR]

The Low Earth Orbit (LEO) region is populated by tens of thousands of objects, which include not only active satellites, but also space debris which can pose a real danger to space operations, and therefore have to be monitored closely. The work presented in this paper addresses the problem of wide field LEO detection and astrometric reduction using a portable, low cost wide field system. The work will address the challenges of astrometric reduction errors for wide FOV images, which lead to the need for multiple calibrations, which pose a challenge for real time operation. We propose a mechanism for aggregating calibration data as the system runs in real time, which does not slow down the real time operation but continuously increases the accuracy of the results. The proposed mechanism can be adapted for both staring and sidereal tracking observation modes, and to all types of astrometry software or streak detection algorithms. [ABSTRACT FROM AUTHOR]

Partial differential equations have recently garnered substantial attention as an image processing framework due to their extensibility, the ability to rigorously engineer and analyse the governing dynamics as well as the ease of implementation using numerical methods. This paper explores a novel approach to image trinarization with a concrete real-world application of classifying regions of sperm images used in the automatic analysis of sperm morphology. The proposed methodology engineers a diffusion equation with non-linear source term, exhibiting three steady-states. The model is implemented as an image processor using a standard finite difference method to illustrate the efficacy of the proposed approach. The performance of the proposed approach is benchmarked against standard image clustering/segmentation methods and shown to be highly effective. • A novel approach to image trinarization using a dynamic PDE based framework. • Data driven Medical Imaging application (Sperm Morphology). • Provably stable method. [ABSTRACT FROM AUTHOR]

Etiology of adolescent idiopathic scoliosis (AIS) is still unknown. Prior in vitro research suggests intervertebral disc pathomorphology as a cause for the initiation and progression of the spinal deformity, however, this has not been well characterized in vivo. To quantify and compare lumbar disc health and morphology in AIS to controls. Cross-sectional study. All lumbar discs were imaged using a 3T MRI scanner. T2-weighted and quantitative T2* maps were acquired. Axial slices of each disc were reconstructed, and customized scripts were used to extract outcome measurements: Nucleus pulposus (NP) signal intensity and location, disc signal volume, transition zone slope, and asymmetry index. Pearson's correlation analysis was performed between the NP location and disc wedge angle for AIS patients. ANOVAs were utilized to elucidate differences in disc health and morphology metrics between AIS patients and healthy controls. α=0.05. There were no significant differences in disc health metrics between controls and scoliotic discs. There was a significant shift in the NP location towards the convex side of the disc in AIS patients compared to healthy controls, with an associated increase of the transition zone slope on the convex side. Additionally, with increasing disc wedge angle, the NP center migrated towards the convex side of the disc. The present study elucidates morphological distinctions of intervertebral discs between healthy adolescents and those diagnosed with AIS. Discs in patients diagnosed with AIS are asymmetric, with the NP shifted towards the convex side, which was exacerbated by an increased disc wedge angle. Investigation of the MRI signal distribution (T2w and T2* maps) within the disc suggests an asymmetric pressure gradient shifting the NP laterally towards the convexity. Quantifying the progression of these morphological alterations during maturation and in response to treatment will provide further insight into the mechanisms of curve progression and correction, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we are interested in digital convexity. This notion is applied in several domains like image processing and discrete tomography. We choose to study the inflation and deflation of digital convex sets while maintaining the convexity property. Knowing that any digital convex set can be read and identified by its boundary word, we use combinatorics on words perspective instead of a purely geometric approach. In this context, we characterize the points that can be added or removed over the digital convex sets without losing their convexity. Some algorithms are given at the end of each section with examples of each process. [ABSTRACT FROM AUTHOR]

Image restoration has been a longstanding challenge in image processing, involving the enhancement of image content to extract valuable information. Nonlinear models have demonstrated their efficacy in eliminating additive noise, which motivates our goal of investigating a novel nonlocal nonlinear reaction-diffusion model for noise removal. The proposed model integrates a fractional diffusion equation with a nonlinear nonlocal p -Laplace operator, with the fidelity term employing a weak norm to better capture oscillatory patterns and intricate details in textured images. Using the Schauder fixed point theorem, the well-posedness of the proposed model solution is established. The experimental results confirm the effectiveness and efficiency of the proposed model, providing validation for its practical utility. [ABSTRACT FROM AUTHOR]

In this work, we propose a physics-informed solution for a blending PDE model with application in image denoising and edge-preserving. The blending model contains isotropic diffusion (ID) model and the modified Perona-Malik model introduced by Catte et al. Then we solve this model applying a physics-informed approach. Solving the new blending model is done using DeepXDE, which is a deep learning library for solving differential equations, by considering the image as input. Error analysis for neural network approximations of the new PDE model is shown. Applying PSNR criteria for some images, it can be seen that the proposed method can compete with solving denoising PDE models using finite difference method (FDM). [ABSTRACT FROM AUTHOR]

The concept of video motion magnification has become increasingly relevant due to its ability to detect small and invisible motions that can be of great value in a variety of applications. A variety of approaches have been developed to magnify these motions and variations. While both Eulerian and Lagrangian processing methods are widely used for motion magnification, Eulerian approaches are more commonly employed due to their lower computational cost. This paper provides an overview of the powerful Eulerian motion magnification techniques. We begin with a brief introduction to technical concepts associated with Eulerian motion techniques such as pyramids and filters in image processing. Additionally, we provide a comparison between the Lagrangian and Eulerian perspectives, followed by a comprehensive overview of the various Eulerian motion magnification (EVM) techniques available. Finally, we present implementation results and a comparative analysis of some of the Eulerian motion techniques. [Display omitted] • Detecting Imperceptible Motions: Explore motion magnification's role in revealing subtle movements. • Demystifying Eulerian Processing: Simplify complex concepts, mathematical foundations. • Real-World Applications: Illustrate motion magnification's utility in healthcare, construction, etc. • Eulerian Advantages: Compare and emphasize the strength of Eulerian over Lagrangian methods. • Comprehensive Survey: Cover a range of Eulerian motion magnification techniques. [ABSTRACT FROM AUTHOR]

Person Search has recently emerged as a challenging task that aims to jointly solve Pedestrian Detection and Person Re-identification (Re-ID). However, the existing approaches still stay at the image processing stage. In this paper, we proposed E3ID, an efficient end-to-end person search model for video, to better solve the person search problem in real densely populated areas. To speed up the model, a low-quality image filter is proposed to adaptively adjust the sampling frequency of Yolov5 according to the moving speed of pedestrians in the video with minimal computational effort. And the cropped pedestrian images are stored in the gallery library for the Re-ID step. Then, we exploit color feature enhancer to mine color features in specific regions that contribute significantly to the Re-ID process. Compared to other state-of-the-art methods, our proposed E3ID can reach 77.94 % on the evaluation index rank 1, which creates a feasibility of using this model in other complex real-world scenarios. Person Search has recently emerged as a challenging task that aims to jointly solve Pedestrian Detection and Person Re-identification (Re-ID). However, the existing approaches still stay at the image processing stage. In this paper, we proposed E3ID, an efficient end-to-end person search model for video, to better solve the person search problem in real densely populated areas. To speed up the model, a low-quality image filter is proposed to adaptively adjust the sampling frequency of Yolov5 according to the moving speed of pedestrians in the video with minimal computational effort. And the cropped pedestrian images are stored in the gallery library for the Re-ID step. Then, we exploit color feature enhancer to mine color features in specific regions that contribute significantly to the Re-ID process. Compared to other state-of-the-art methods, our proposed E3ID can reach 77.94 % on the evaluation index rank 1, which creates a feasibility of using this model in other complex real-world scenarios. [Display omitted] [ABSTRACT FROM AUTHOR]

Image deraining aims to remove rain streaks from images and reduce information loss in outdoor images caused by rain. As a fundamental task in image processing, image deraining not only enhances the visibility of images but also provides necessary image restoration for advanced vision tasks. Existing image deraining models mostly train end-to-end models by minimizing the similarity between the output image of the model and the rain-free ground truth. Although these methods have achieved significant results, they often perform poorly in the face of dense and changing rain streak scenes. In this paper, we propose a novel method, called D ual-Channel C omponent D ecomposition Net work (DCD-Net). The basic idea of DCD-Net is to leverage the separability prior of rainy images, treats the rain-free background layer and the rain streak mask layer as two parallel component extraction tasks. To this end, it builds a dual-branch parallel networks that extract the rain-free background image and decouple the reconstruction information of the rain streak mask, respectively. It finally applies a composite multi-level contrastive supervision to the output of the above dual-branch parallel network, thereby achieving rain streak removal. Extensive experiments on various datasets demonstrate that the proposed model outperforms existing methods in deraining dense rain streak images. [Display omitted] • This paper proposes an image deraining method, called Dual-Channel Component Decomposition Network (DCD-Net). • DCD-Net treats the rain-free background layer and the rain streak mask layer as two parallel component extraction tasks. • DCD-Net obtains competitive performance in deraining complex and dense rain streak images. [ABSTRACT FROM AUTHOR]

Images taken under poor lighting conditions generally present problems such as low brightness, noise, and color distortion, so dealing with low-light images is challenging. To solve this problem, current mainstream methods focus on designing special deep networks. For example, many methods are based on Retinex-based models. However, the solution of the Retinex model is an ill-conditioned problem in mathematics, so these methods are difficult to obtain the expected result. In this paper, following the new paradigm of embedding prior knowledge in deep learning, we design a simple yet surprisingly effective neural network, called Multi-Resolution Edge-aware Lighting Enhancement Network (MELENet). Since resolution and acutance are two of the key factors related to the visual quality of natural images, MELENet with a dual-branch structure aims to incorporate multi-resolution edge information into deep learning for guiding lighting enhancement. Additionally, by adopting special loss functions, the model suppresses noise and prevents color distortion while obtaining sound lighting enhancement effects. Experiments have verified that such a simple solution exhibits outstanding performance compared to existing mainstream lighting enhancement methods. We also directly apply the proposed model to image de-hazing and obtain state-of-the-art results. [Display omitted] • The proposed algorithm focuses on embedding prior knowledge into the network to assist the low-light image enhancement. • An effective loss function is designed to balance the color distortion and noise suppression. • Our model can be directly applied to other image processing tasks such as image de-hazing and gain competitive results. [ABSTRACT FROM AUTHOR]

It is well-understood that the performance of Deep Convolutional Neural Networks (DCNNs) in image recognition tasks is influenced not only by shape but also by texture information. Despite this, understanding the internal representations of DCNNs remains a challenging task. This study employs a simplified version of the Portilla-Simoncelli Statistics, termed "minPS," to explore how texture information is represented in a pre-trained VGG network. Using minPS features extracted from texture images, we perform a sparse regression on the activations across various channels in VGG layers. Our findings reveal that channels in the early to middle layers of the VGG network can be effectively described by minPS features. Additionally, we observe that the explanatory power of minPS sub-groups evolves as one ascends the network hierarchy. Specifically, sub-groups termed Linear Cross Scale (LCS) and Energy Cross Scale (ECS) exhibit weak explanatory power for VGG channels. To investigate the relationship further, we compare the original texture images with their synthesized counterparts, generated using VGG, in terms of minPS features. Our results indicate that the absence of certain minPS features suggests their non-utilization in VGG's internal representations. [ABSTRACT FROM AUTHOR]

• We propose a new plug-and-play deep neural network method to remove Rician noise. • We prove some mathematical properties and the convergence of the proposed method. • Experimental results show that the proposed method outperforms existing methods. Restoring images corrupted by Rician noise is a challenging issue in medical image processing. In the existing methods, the model-driven method can not recover the images well, and the learning-based methods lack good interpretability. In this paper, we propose a plug-and-play (PnP) method to remove Rician noise. Due to the statistical properties of Rician distribution and the implicit deep image priors, the problem is non-convex. We present a convergent PnP method to address these issues by an adaptively relaxed alternating direction method of multipliers. Theoretically, we give some useful mathematical properties and the global linear convergence of the proposed method by an adaptive relaxation strategy. Experimental results show that the proposed method outperforms the existing state-of-art traditional and learning-based methods. [ABSTRACT FROM AUTHOR]

Advances in remote sensing image processing techniques have further increased the demand for annotated datasets. However, preparing annotated multi-temporal 2D/3D multimodal data is especially challenging, both for the increased costs of the annotation step and the lack of multimodal acquisitions available on the same area. We introduce the Simulated Multimodal Aerial Remote Sensing (SMARS) dataset, a synthetic dataset aimed at the tasks of urban semantic segmentation, change detection, and building extraction, along with a description of the pipeline to generate them and the parameters required to set our rendering. Samples in the form of orthorectified photos, digital surface models and ground truth for all the tasks are provided. Unlike existing datasets, orthorectified images and digital surface models are derived from synthetic images using photogrammetry, yielding more realistic simulations of the data. The increased size of SMARS, compared to available datasets of this kind, facilitates both traditional and deep learning algorithms. Reported experiments from state-of-the-art algorithms on SMARS scenes yield satisfactory results, in line with our expectations. Both benefits of the SMARS datasets and constraints imposed by its use are discussed. Specifically, building detection on the SMARS-real Potsdam cross-domain test demonstrates the quality and the advantages of proposed synthetic data generation workflow. SMARS is published as an ISPRS benchmark dataset and can be downloaded from https://www2.isprs.org/commissions/comm1/wg8/benchmark_smars/. • SMARS is a synthetic dataset for urban object classification/3D change detection. • SMARS includes simulated orthophotos with DSMs and reference data. • Novel workflow for synthetic 2D/3D multimodal multi-temporal datasets preparation. • Assessment of the feasibility of SMARS for real applications. • First attempt at building extraction on synthetic-real cross-domain multimodal data. [ABSTRACT FROM AUTHOR]

• An efficient algorithm for applying the non-homogeneous Tikhonov filter. • Our method uses iterations of a dense matrix computed implicitly through recursive filters. • An efficient algorithm for applying the homogeneous Tikhonov filter. • Our method uses a novel optimization scheme for efficiently decomposing the homogeneous kernel as a sum of Gaussians. • A discussion on the precision of our methods and the execution times of our implementations, available as open source. In this work, we describe an implementation of the 2D Tikhonov regularization filter which scales linearly with the input signal's size. In the homogeneous case, we propose a novel algorithm to decompose the filter's 2D kernel as a sum of axis-aligned Gaussians. Our algorithm uses symmetries of the kernel to provide a fast computation of the Gaussian decomposition in the frequency domain, where the 2D Tikhonov kernel has a closed-form expression. The convolution with each Gaussian is then computed using linear-time separable recursive filtering. In the non-homogeneous case, we also decompose the 2D problem as a series of iterated linear-time separable recursive filters, which can be combined with the Biconjugate Gradient Stabilized method for fast convergence. In this way, a fast solution to the 2D Tikhonov regularization problem is obtained. [ABSTRACT FROM AUTHOR]

• A computational binary characterization method for convex object(s). • A unified model for convex object(s) segmentation and convex hull computation. • Linearization technique employed to reduce the difficulty of the model solving. • A convergent proximal alternating direction method of multipliers for the linearization problem. • An interactive procedure to improve the segmentation accurac y in terms of shape distance and D ice coefficient. Convexity prior is one of the main cues for human vision and shape completion with important applications in image processing and computer vision. This paper provides a computable characterization method for shape convexity, and illustrates its applications in image segmentation and convex hull computation. We prove that the convexity of a region is equivalent to a series of quadratic inequality constraints on its indicator function. By incorporating this result, models are proposed for image segmentation with convexity prior and convex hull computation of clean and noisy data sets, respectively. Then, these models are summarized into a unified optimization problem on binary function(s) with the quadratic inequality constraints. Numerical method is proposed by linearizing the quadratic constraints. The linearization problem is solved by a proximal alternating direction method of multipliers, the convergence of which is guaranteed under some proper conditions. Numerical experiments demonstrate the efficiency and effectiveness of the proposed methods for image segmentation and convex hull computation. [ABSTRACT FROM AUTHOR]

• A soil clod recognition system is developed based on depth images. • The validity of the system is confirmed using a scale model of tilling test. • Tilling mechanism is analyzed using the proposed system. • Experimental results are compared with simulation using the proposed system. • Results confirm that the proposed method can accurately recognize soil clods. Rotary claws are used to break soil into small pieces and plow the soil flat. In this study, rotary claw development in tiller machines was evaluated using both equipment and the discrete element method (DEM). However, evaluation through equipment is imprecise and time consuming. Although DEM is an effective method for modeling the movement of granular materials, it requires numerous parameters to ensure accuracy, which must be determined through experimental evaluation and comparison. To resolve this concern, an image processing method was developed that leverages point cloud data obtained from a depth camera to capture changes in soil shape, distribution, and soil clod size before and after tilling. The effect of soil moisture content and claw rotation speed on soil clod formation and decomposition was evaluated. The experimental results show that the location, number, and soil clod size vary with soil moisture content and claw rotation speed. The results were compared with the DEM simulation to reconcile differences and confirm the feasibility of the proposed method. The model experiment system for soil clods and the image processing method facilitates a quantitative comparison between experimental and DEM simulated soil dispersal, which accelerates the search for DEM parameters to reproduce the tilling. [ABSTRACT FROM AUTHOR]

Hera mission is the European Space Agency's contribution to the international collaboration with NASA for the planetary defence, i.e. Asteroid Impact Deflection Assessment aiming to deflect the trajectory of its target binary asteroid system (65803) Didymos. The Early Characterization Phase and the Detailed Characterization Phase of Hera mission are two phases of the proximity operations with the objective to physically and dynamically characterize Didymos. During these phases, an Image Processing algorithm is required to estimate the position of the centroid of the primary to enable Line of Sight navigation. However, the performance of standard Image Processing algorithms is affected by the disturbances of the image, such as poor illumination conditions, the presence of external bodies and the irregular shape of the target. This research addresses this challenge by developing a robust Convolutional Neural Networks-based Image Processing algorithm to estimate the position of the centroids of Didymos and its moon Dimorphos, the pseudorange from the primary and the Sun phase angle. The training, validation and testing datasets are generated with the software Planet and Asteroid Natural scene Generation Utility using the Early Characterization Phase and the Detailed Characterization Phase trajectories as case scenario. The position in the image of the centroids of Didymos and Dimorphos is estimated using their respective position vectors. To estimate the pseudorange, the developed algorithm regresses a set of keypoints on the visible border of Didymos and evaluates its apparent radius. For the Sun phase angle, the pixel position of the subsolar point of the primary is leveraged. The High-Resolution Network is the Convolutional Neural Network architecture applied to detect keypoints with superior spatial precision. Even with the considered disturbances, the analysis shows that the proposed algorithm is able to provide an accurate estimation of the mentioned outputs for all the Early Characterization Phase trajectory and for 77.33% of the Detailed Characterization Phase trajectory, improving the robustness and autonomy of the mission navigation. • The Image Processing of Hera is investigated using Convolutional Neural Networks. • The centroid regression of both Didymos and Dimorphos is solved. • The range and the Sun phase angle are estimated using the pinhole camera model. • The subsolar point position on the surface of Didymos is estimated. [ABSTRACT FROM AUTHOR]

In this experiment, Ni–P nanoparticles were deposited (ED) on SiC micro- and nanoparticles with different parameters. Our goal was to successfully prepare metal deposits and develop an effective method for comparing and evaluating the various procedures. During the experimental work, a three-step electroless Ni–P coating process was applied with different concentrations. The coated SiC particles were examined by scanning electron microscopy (SEM). The mass-specific surface area (SSA) of the coated SiC was measured by the Brunauer‒Emmett‒Teller (BET) method, while the volumetric-specific surface area (VSSA) was also calculated. The adhesion between the metal and the ceramic particle was analyzed by X-ray photoelectron spectroscopy (XPS). An image processing macroprogram was created (with a machine learning-based Trainable Weka Segmentation algorithm) to segment the SEM images of the ED metal particles to calculate the specific surface area (S V). [Display omitted] • Electroless Ni–P particles were deposited on SiC micro- and nanoparticles with different parameters. • X-ray photoelectron spectroscopy (XPS) was used to analyze the adhesion between the metal and ceramic particles. • A machine learning-assisted image processing macro program was created to segment the ED metal particles. • SSA (m2/g), VSSA (m2/cm3), S V (m2/cm3) and distribution of metal particle diameter were compared. [ABSTRACT FROM AUTHOR]

The objective of this study was to distinguish banana samples subjected to 18 different combinations of vacuum drying and various pretreatments using innovative models built based on image textures. Banana slices were either untreated, pretreated by exposure to ultrasound or microwaves, or pretreated by immersion in 5% ascorbic acid or 5% citric acid solution. Samples were then dried in a vacuum dryer at 70 °C and one of three pressures: atmospheric, 200 mmHg of vacuum or 400 mmHg of vacuum. Samples were imaged and classified using a machine learning approach based on 1629 texture parameters in nine color channels. The dried banana samples were classified with high overall accuracies reaching 96.89% for a model including combined selected textures from images in all color channels developed using the Random Forest algorithm from the group of Trees. The confusion matrix revealed the greatest mixing of cases between banana slices dried at 70 °C + gum arabic pretreatment vs. 70 °C + microwave pretreatment and 70 °C+ 200 mmHg + gum arabic pretreatment vs. 70 °C+ 200 mmHg + microwave pretreatment. The successful results showed that the assessment of the influence of drying and pretreatment on banana slices could be carried out using image processing and machine learning in a non-destructive, objective, and effective manner. [ABSTRACT FROM AUTHOR]

• An end-to-end method named PLM-SegNet was proposed for foreground segmentation. • A desirable performance can be obtained with a small amount of training data. • PLM-SegNet is a fast and accurate method with good generalization ability. As a virtual restoration tool, digital imaging is widely used to share the patrimony and preserve the original material of ancient documents. However, the captured raw images usually consist of multiple backgrounds, influencing subsequent image processing, especially the damage investigation process. In this study, an end-to-end method named PLM-SegNet was proposed for the p alm- l eaf m anuscript (PLM) seg mentation based on U- Net. Two cameras (Nikon and Sony) were used to capture 83 palm-leaf manuscript images. The images were labeled by the software Labelme and were then cropped into patches with a specific size to train, validate, and test the PLM-SegNet model. The patch was fed into PLM-SegNet, and the foreground distribution map of this patch was obtained. The foreground distribution map of each patch in an image was predicted and stitched together into one global foreground distribution map. With assistance from the distribution map, the PLM can be segmented from the image easily. The results on two independent test sets showed pixel accuracy of 99.73% and 98.36%, intersection over union (IoU) of 99.42% and 98.31%, Recall of 99.68% and 99.95%, and F1-Score of 99.70% and 99.15% could be achieved, respectively. Additionally, damage detection was adopted as a case study to show the significance of PLM-SegNet. Compared with the raw PLM images, the performance of damage detection on segmented PLM images was improved by 15.00% and 19.33% on F1-Score and IoU, respectively. The results show that PLM-SegNet is a precise and automated method for palm-leaf manuscript segmentation when the labeled training data is limited. The source code is available at https://github.com/Ryan21wy/PLM-SegNet. [ABSTRACT FROM AUTHOR]